GEOG 566






         Advanced spatial statistics and GIScience

April 6, 2018

It’s all in the timing: Assessing risk of an introduced insect on a native plant through investigations of phenological synchrony

Filed under: 2018,My Spatial Problem @ 10:39 pm

1. Research Question / Background:

Biological control of weeds involves introducing or augmenting natural enemies, such as insect herbivores, for population control of a target weed. Choosing insect herbivores that are highly specific to their host plants gives managers some confidence that these species will be safe & effective as biocontrol agents. However, our ability to predict outcomes of introductions is imperfect, and resulting risks to non-target native plants must be weighed in evaluating success & safety of biological control programs.

The cinnabar moth was released in Western Oregon beginning in the 1970s to 1990s to control a European weed, tansy ragwort. However, redistribution of the cinnabar moth was halted after it established on Senecio triangularis, a non-target native wildflower (Diehl and McEvoy, 1988). The moth has established and maintained populations on S. triangularis even in absence of the ancestral host. Cinnabar herbivory of foliage has not been found to cause long term decreases to plant fitness or reproduction (Rodman, 2017). Herbivory of flowers is less common given a mismatch between the flowering time of S. triangularis and peak feeding stages for the moth, but when it does occur, floral/seed herbivory may have direct impacts on population dynamics. Previous work has shown that that larvae experiencing phenological synchrony with S. triangularis flowers decreased seed set by 95% (Rodman, 2017); and that S. triangularis is seed-limited, so that reduction of seed set decreases seedling recruitment to the next generation (Lunde, unpublished data).

Because this plant is a novel host-plant, and because insects and plants can respond to disparate environmental cues to determine the timing of their life cycles (phenology), we expect to see phenological synchrony varying depending on environmental factors. Knowing which populations of S. triangularis would be most likely to experience seed herbivory by cinnabar larvae could help managers track and respond to cinnabar moth presence and the risk to S. triangularis on a site-by-site basis.

This project seeks to explore how variation in phenological synchrony is related to a set of environmental variables for a set of known populations of cinnabar moth on Senecio triangularis. Using a linear mixed model, and environmental variables measured directly or derived from zonal statistics of spatial data, we will use variable selection processes to determine which candidate factors best explain variation in phenological synchrony seen on a single date (July 22) in 2017. This relationship will then be used to predict phenological synchrony, and subsequent risk of seed herbivory, for a larger set of 26 sites with known S. triangularis populations extending across the Oregon Cascade & Coast Ranges.

2. Data:

This project uses a dataset of phenology scores (for cinnabar larvae and Senecio triangularis) and environmental variables taken for five populations in the Willamette National Forest near Oakridge, OR. The project also estimates environmental variables from assorted spatial datasets. Though some data were taken at the level of individual plants, all variables will be considered at the site level. Some variables (such as soil type, snowmelt date) cannot be measured and deemed meaningful at the level of individual plants. Data sources/details as follows:

Population locations: two polygon layers — one includes 5 sites surveyed over the 2017 season; one includes 26 additional sites previously surveyed for cinnabar presence and host-plant damage.

Phenological synchrony: tabular, count data for number of vulnerable and invulnerable flower heads (capitula), and counts of cinnabar larvae in peak feeding stages (4th & 5th instars), collected from a random subset of tagged plants on July 22, 2017. These count data will be used to derive a measure of phenological synchrony for each site. While data were collected at 10-day intervals, in order to capture variation in synchrony, this analysis will focus on data from July 22. This date is halfway between the date at which all capitula were vulnerable and the date at which all capitula were invulnerable in 2017.

The following environmental variables are included in the phenology dataset: ambient temperature at 6 inches and 36 from the ground; soil temperature and moisture at 6 inch depth; and approximate sun exposure measured by a Solar Pathfinder. These data are all constrained to the 5 surveyed sites.

Snow disappearance date will be approximated from a model developed by Ann Nolin and the Mountain Hydroclimatology Group. This model uses MODIS data to determine date of snow disappearance at a resolution of approximately 500m. Accumulated degree days will be approximated for July 22, 2017 from a model developed by Len Coop of the OSU Integrated Plant Protection Center (IPPC), based on a variety of climate data: AGRIMET, HYDROMET, ASOS/METAR and COOP networks, RAWS network, SNOTEL network, and others.

Soil type for each site will be determined from SSURGO soil layers, which provide polygon boundaries of component soil types as determined by the National Cooperative Soil Survey. Elevation and aspect (site average) will be determined from elevation and aspect surface previously developed from a Digital Elevation Model of Oregon at a 30m resolution. Each layer will be obtained for the extent of Oregon, and considered at the scale of 31 identified sites using zonal statistics.

3. Hypotheses:

The phenology of insects is usually predicted on degree days, because insects are ectotherms whose development is largely constrained by external heat gain (Johnson et al., 2007); whereas alpine and subalpine species of perennial plants have been shown to vary widely in terms of which environmental factors drive flowering phenology (Dunne et al., 2003). We expect to see differences in phenological synchrony of host-plant and larvae to the extent that environmental drivers underlying each species vary independently.

My hypothesis is that phenological synchrony varies between sites on July 22, 2017, and that a significant amount of the variation in phenological synchrony can be explained by a combination of candidate environmental factors (listed above). Further, I hypothesize that this model can be used to predict risk of cinnabar seed herbivory to S. triangularis based on values of environmental factors.

It is possible that the five sites represented in the available data do not represent a wide enough range in explanatory variables to adequately test this hypothesis. In this case, I would seek to approximate what site conditions need to be represented in the data in order to answer the research question.

4. Approaches:

Initial analysis will use zonal statistics and statistical analysis of tabular data to determine the variation in phenological synchrony and candidate explanatory variables across 5 sites surveyed in 2017. This will be used to develop a linear mixed model using appropriate variable selection methods to determine which environmental factors best explain variation in phenological synchrony for July 22.

Then, using a broader set of 26 sites with known S. triangularis populations and the same set of environmental data, I will use the linear mixed model to predict phenological synchrony for theoretical cinnabar moth populations at these sites. Results of this analysis would be used to display the estimated phenological synchrony, and risk of seed herbivory, for all 31 sites.

5. Expected outcome:

Outcomes for this project will be a linear mixed model that can predict mid-season phenological synchrony of cinnabar larvae and S. triangularis flowers from a set of environmental explanatory variables derived from spatial datasets and 2017 phenology survey data.

An additional product will be a map showing phenological synchrony for 26 additional sites as predicted from environmental factors deemed significant in this first part. Not all of these populations have cinnabar moth populations, but this map would allow us to identify sites at which S. triangularis would be at high or low risk of seed herbivory if populations of cinnabar moth were to establish.

6. Significance:

Estimations and predictions of phenological synchrony determined in this study will be significant in answering how often and under what conditions cinnabar larvae have the potential to decrease seed set for Senecio triangularis through floral herbivory. Experimental data could be used to estimate decreases in annual seedling recruitment based on seed reduction scenarios. Meanwhile, a map showing relative risk of seed herbivory due to phenological synchrony will allow managers to identify high-risk populations of S. triangularis in order to focus monitoring efforts at these sites and possibly intervene by reducing or moving cinnabar moth populations.

7. Level of preparation:

ArcINFO: 3 terms of coursework (GIS I, II, & III) and independent work; relatively confident.

Modelbuilder and/or GIS programming in Python: one term of coursework (GIS III); somewhat confident.

R: three terms coursework (Stats 511, 512 & FES 524); limited proficiency, no experience with spatial data

WORKS CITED

Diehl, J.W., and McEvoy, P.B. (1988). Impact of the Cinnabar Moth (Tyria jacobaeae) on Senecio triangularis, a Non-target Native Plant in Oregon. In Proceeding VII International Symposium on Biological Control of Weeds, (Rome, Italy), p. 119-126.

Dunne, J.A., Harte, J., and Taylor, K.J. (2003). Subalpine Meadow Flowering Phenology Responses to Climate Change: Integrating Experimental and Gradient Methods. Ecol. Monogr. 73, 69–86.

Johnson, D., Bessin, R., and Townsend, L. (2007). Cooperative Extension Service, University of Kentucky. Resource 474, 7727.

McEvoy, P.B., Higgs, K.M., Coombs, E.M., Karaçetin, E., and Ann Starcevich, L. (2012). Evolving while invading: rapid adaptive evolution in juvenile development time for a biological control organism colonizing a high-elevation environment. Evol. Appl. 5, 524–536.

Rodman, M. (2017). Non-target Effects of Biological Control: Ecological Risk of Tyria jacobaeae to Senecio triangularis in Western Oregon. Oregon State University.

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1 Comment

  1.   jonesju — April 9, 2018 @ 8:43 am    

    hi Katarina,
    Thanks for your description of your spatial problem. The variable of interest (dependent variable) seems to be the phenological stage of your 31 surveyed populations as of July 22, 2017. If so, I suggest you start by creating a GIS layer showing the locations of these surveyed sites, and that you attribute each of the surveyed points with information about the numbers of plants, their phenological stage, the presence of cinnabar moth larvae or other life stages of the moth, etc.

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